Comprehensive and Dedicated Metrics for Evaluating AI-Generated Residential Floor Plans

In response to the growing importance of AI-driven residential design and the lack of dedicated evaluation metrics, we propose the Residential Floor Plan Assessment (RFP-A), a comprehensive framework tailored to architectural evaluation. RFP-A consists of multiple metrics that assess key aspects of floor plans, including room count compliance, spatial connectivity, room locations, and geometric features. It incorporates both rule-based comparisons and graph-based analysis to ensure design requirements are met. A comparison of RFP-A and existing metrics was conducted both qualitatively and quantitatively, and it was revealed that RFP-A provides more robust, interpretable, and computationally efficient assessments of the accuracy and diversity of generated plans. We evaluated the performance of six existing floor plan generation models using RFP-A, showing that, surprisingly, only HouseDiffusion and FloorplanDiffusion achieved accuracies above 90%, while other models scored below or around 60%. We further conducted a quantitative comparison of diversity, revealing that FloorplanDiffusion, HouseDiffusion, and HouseGAN each demonstrated strengths in different aspects—graph structure, spatial location, and room geometry, respectively—while no model achieved consistently high diversity across all dimensions. In addition, existing metrics can not reflect the quality of generated designs well, and the diversity of the generated designs depends on both the model input and structure. Our study not only enhances the assessment of generated floor plans but also aids architects in utilizing numerous generated designs effectively.